Apoptosis gene polymorphisms, age, smoking and the risk of non-small cell lung cancer
Bibliographic record
Abstract
Apoptosis is important for targeting cancer cells for destruction. Various single-nucleotide polymorphisms (SNPs) in apoptotic genes have been associated with increased risks in lung cancer, particularly FAS -1377 G>A (rs2234767), FASLG -844 C>T (rs763110), IL1B +3954 C>T Phe105Phe (rs1143634) and BAT3 Ser625Pro (rs1052486). We studied the association of these SNPs with non-small cell lung cancer (NSCLC) in a large case-control study (N = 4263: 2644 cases and 1619 controls). No associations with NSCLC were observed in the main effects analysis for all four SNPs, adjusting for age, gender, smoking status, pack-years and years since smoking cessation. In subjects under age 60, for FASLG -844 C>T polymorphism, CT compared with the CC genotype, was significantly associated with increased risk of NSCLC, adjusted odds ratio (aOR) = 1.58 (1.22, 2.05), P = 0.0006 and TT aOR = 1.45 (1.01, 2.04), P = 0.04. In contrast, for those over age 60, the CT aOR = 0.91 (0.73, 1.13), P = 0.37 and TT aOR = 0.86 (0.64, 1.16), P = 0.32. The P-value for the age-genotype interaction was 0.004. For the IL1B +3954 C>T polymorphism, compared with the CC genotype, TT showed significant associations in former smokers and in men but tests of interaction were not significant (P(smoking) = 0.24, P(gender) = 0.17). No interactions were observed for FAS -1377 G>A and BAT3 Ser625Pro polymorphisms. Our findings indicate that age and smoking may modify the association of the FASLG -844 and IL1B + 3954 SNPs with the risk of NSCLC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".